Combined effect of surface PM2.5 assimilation and aerosol-radiation interaction on winter severe haze prediction in central and eastern China

Abstract

In this study, the effects of aerosol-radiation interaction (ARI) and data assimilation (DA) during a haze episode in December 2016 are evaluated by four sets of parallel experiments using the atmospheric chemical model GRAPES_Meso5.1/CUACE. The results show that although the BASE experiment without ARI and DA generally captures the variations of particulate matter (PM) and visibility (VIS), it dramatically underestimates the high PM concentrations and overestimates the low VIS on severe haze days. Moreover, the ARI effect is not significant when the aerosol concentration is relatively low (PM2.5 <150 μgm−3). Assimilation of surface PM2.5 corrects the model chemical initial conditions (ICs) and increases the aerosol concentration in severe haze areas. Focusing on the severe pollution periods, the combined effect of ARI and DA significantly improves forecast accuracy and prolongs this improvement, which reduces the average negative biases of PM2.5 and PM10 by 33% and 35%, and increases the average correlation coefficients by 14% and 12%. Moreover, this combined effect also changes the vertical distribution of the atmosphere, especially at a low level, leading to a 2.6 K decrease in potential temperature (PT) and a 7.6% increase in relative humidity (RH) below 300 m. Due to the contributions of PM and RH, the low VIS prediction is significantly improved, with the root mean square error (RMSE) reduced by 30%. The study results show the combined effect of ARI and DA on aerosols and meteorology and suggest the importance of considering ARI and DA simultaneously in the atmospheric chemical model.